Coconuts and coconut products are an important commodity in the Tongan economy. Plantations, such as the one in the town of Kolovai, have thousands of trees. Inventorying each of these trees by hand would require lots of time and manpower. Alternatively, tree health and location can be surveyed using remote sensing and deep learning. In this lesson, you'll use the Deep Learning tools in ArcGIS Pro to create training samples and run a deep learning model to identify the trees on the plantation. Then, you'll estimate tree health using a Visible Atmospherically Resistant Index (VARI) calculation to determine which trees may need inspection or maintenance.

To detect palm trees and calculate vegetation health, you only need ArcGIS Pro with the Image Analyst extension. To publish the palm tree health data as a feature service, you need ArcGIS Online and the Spatial Analyst extension.

In this lesson you will build skills in these areas:

• Creating training schema
• Digitizing training samples
• Using deep learning tools in ArcGIS Pro
• Calculating VARI
• Extracting data to points

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This resource contains flood extent maps of the Huallaga River near Chazuta, Peru derived from Height Above Nearest Drainage (HAND). MERIT Hydro global hydrography datasets (Hydrologically Adjusted Elevations, Flow Direction, and Upstream Drainage Pixel) were used as the input terrain data. The HAND raster and corresponding rating curve were creating using ArcHydro Pro tools in ArcGIS Pro. Specifically, this resource has a shapefile of flood extents for HAND values between 0-15, catchment and drainage line shapefiles, the HAND raster, and the rating curve as a csv. Using a flowrate, the HAND value can be determined from the rating curve. Then, the flood extent is the feature in the Chazuta_FloodExtent_HAND shapefile with the FloodValue attribute matching the Height value from the rating curve. As an example, this resource also includes a shapefile of the 5-m (Height value) flood extent along with the corresponding flood depth raster.